#coding=utf-8
import csv
import random
import math
import operator

def loadDataset(filename, split, trainingSet=[], testSet=[]):
    with open(filename, "rt") as csvfile:
        lines = csv.reader(csvfile)
        dataset = list(lines)
        for x in range(len(dataset)-1):
            for y in range(4):
                dataset[x][y] = float(dataset[x][y])
            if random.random() < split:
                trainingSet.append(dataset[x])
            else:
                testSet.append(dataset[x])

def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x] - instance2[x]), 2)
    return math.sqrt(distance)

def getNeightbors(trainingSet, testInstance, k):
    distances = []
    length = len(testInstance) - 1
    for x in range(len(trainingSet)):
        dist = euclideanDistance(testInstance, trainingSet[x], length)
        distances.append((trainingSet[x], dist))
    distances.sort(key=operator.itemgetter(1))
    neightbors = []
    for x in range(k):
        neightbors.append(distances[x][0])
    return neightbors

def getResponse(neightbors):
    classVotes = {}
    for x in range(len(neightbors)):
        response = neightbors[x][-1]
        if response in classVotes:
            classVotes[response] += 1
        else:
            classVotes[response] = 1
    sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
    return sortedVotes[0][0]

def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][-1] == predictions[x]:
            correct += 1
    return (correct / float(len(testSet))) * 100.0

if __name__ == '__main__':
    trainingSet = []
    testSet = []
    split = 0.67
    loadDataset(r'C:\Users\xiaoyu\PycharmProjects\DeepLearning\iris.data.txt', split, trainingSet, testSet)
    print("Train set: " + repr(len(trainingSet)))
    print("Test set: " + repr(len(testSet)))

    predictions = []
    k = 3
    for x in range(len(testSet)):
        neighbors = getNeightbors(trainingSet, testSet[x], k)
        result = getResponse(neighbors)
        predictions.append(result)
        print("predictions= " + repr(result) + ', actual= ' + repr(testSet[x][-1]))
    accuracy = getAccuracy(testSet, predictions)
    print('Accuracy: ' + repr(accuracy) + '%')



